In digital images, “Thinning” is usually considered as one of the morphological operators which requires the input image to be binary instead of grayscale. In addition, Thinning is a sequential operator, requiring multiple operations corresponding to a sequence of structuring elements with different orientations. A comprehensive survey about thinning methodologies can be found in Louisa Lam, Seong-Whan Lee, and Ching Y. Suen, “Thinning Methodologies—A Comprehensive Survey,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 14, No. 9, September 1992, pp. 869-885. Other thinning methods with some variations where the input is grayscale image instead of binary image could be found in: Sabrina Rami-Shojaei and Corinne Vachier, “H-Thinning for Gray-Scale Images,” International Conference on Image Processing 2004, pp. 287-290; Mark E. Hoffman and Edward K. Wong, “Scale-Space to Image Thinning Using the Most Prominent Ridge-Line in the Pyramid Data Structure,” Proc. SPIE—Document Recognition V, Vol. 3305, April 1998, pp. 242-252; and Jocelyn Marchadier, Dider Arques, and Sylvain Michelin, “Thinning Grayscale Well-Composed Images,” Pattern Recognition Letters, Vol. 25, 2004, pp. 581-590.
However, in the morphological sense, the thinning method where input could be either binary or grayscale image generates a minimally connected line that is equidistant from the boundaries and results in the skeleton of each object or line in the input image. In certain applications, the resulting skeleton is a useful topological representation of an object; however, it is not the case for the image enhancement application since the geometric shape of thinned lines/objects are not preserved.